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＜電子ブック＞
Linear Programming Using MATLAB® / by Nikolaos Ploskas, Nikolaos Samaras
(Springer Optimization and Its Applications. ISSN:19316836 ; 127)

版	1st ed. 2017.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2017
本文言語	英語
大きさ	XVII, 637 p. 59 illus., 47 illus. in color : online resource
著者標目	*Ploskas, Nikolaos author Samaras, Nikolaos author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Computer software LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Algorithms FREE:Continuous Optimization FREE:Mathematical Software FREE:Mathematical Applications in Computer Science FREE:Algorithms
一般注記	1. Introduction -- 2. Linear Programming Algorithms -- 3. Linear Programming Benchmark and Random Problems -- 4. Presolve Methods -- 5. Scaling Techniques -- 6. Pivoting Rules -- 7. Basis Inverse and  Update Methods -- 8. Revised Primal Simplex Algorithm -- 9. Exterior Point Simplex Algorithms -- 10. Interior Point Method -- 11. Sensitivity Analysis -- Appendix: MATLAB’s Optimization Toolbox Algorithms --  Appendix: State-of-the-art Linear Programming Solvers;CLP and CPLEX This book offers a theoretical and computational presentation of a variety of linear programming algorithms and methods with an emphasis on the revised simplex method and its components. A theoretical background and mathematical formulation is included for each algorithm as well as comprehensive numerical examples and corresponding MATLAB® code. The MATLAB® implementations presented in this book  are sophisticated and allow users to find solutions to large-scale benchmark linear programs. Each algorithm is followed by a computational study on benchmark problems that analyze the computational behavior of the presented algorithms. As a solid companion to existing algorithmic-specific literature, this book will be useful to researchers, scientists, mathematical programmers, and students with a basic knowledge of linear algebra and calculus.  The clear presentation enables the reader to understand and utilize all components of simplex-type methods, such as presolve techniques, scaling techniques, pivoting rules, basis update methods, and sensitivity analysis HTTP:URL=https://doi.org/10.1007/978-3-319-65919-0

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